Systems and methods for improving smart city and smart region architectures

ABSTRACT

Improved systems, methods, and architectures to enhance decision making in Smart Cities and Smart Regions. A system includes an index structure including a first hierarchical data structure including a first hierarchical score based on a plurality of first-level elements, each of the plurality of first-level elements having a respective weighting, and a second hierarchical data structure including a plurality of second hierarchical scores based on a plurality of second-level elements, each of the plurality of second-level elements having a respective weighting, such that the first hierarchical score is based on the plurality of second hierarchical scores through an index factor; and a computer-implemented regional monitor engine to manage local access to a plurality of external data sources to coordinate writes to the index structure.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of U.S. ProvisionalApplication No. 62/791,395, filed Jan. 11, 2019, which is fullyincorporated by reference herein. The present application is alsorelated to U.S. Patent Application Publication No. 2003/0135599 (U.S.application Ser. No. 10/256,532) filed Sep. 27, 2002, and U.S. PatentApplication Publication No. 2016/0171425 (U.S. application Ser. No.14/966,153) filed Dec. 11, 2015, each of which is incorporated herein inits entirety.

TECHNICAL FIELD

Embodiments relate generally to systems, methods, and architectures forimproving Smart Cities and Smart Regions. More particularly, embodimentsimprove connectivity and data collection for Smart Cities and SmartRegions.

BACKGROUND

A Smart City is an urban area that uses different types of electronicdata collection sensors to supply information which is used to manageassets and resources efficiently. The goal of the Smart City is togather information to enhance decision making. Further, by integratingrural and urban economies, a Smart Region can be created. There is aneed for improved systems, methods, and architectures to enhancedecision making in these areas.

Technological developments have become ubiquitous with modern societybut the integration of these advancements is often slow and cumbersome.There is a need for improved systems, methods, and architectures tobetter implement new technologies in Smart Cities and Smart Regionswhile preventing unequal access to information. To compound these needs,governments at many levels use organizational structures that encouragedata silos, mitigating the spread of information and leverage ofresources to address complex problems. Without proper sharing of data,both among and within governments, it can be challenging for citizenryto effectively make informed decisions for their community or to asserttheir role in civic leadership.

SUMMARY

An improved Smart Region architecture can support and leverage newtechnologies in a uniform implementation throughout a Smart City orSmart Region.

In an embodiment, a system for coordinating a plurality of data sourcescomprises an index structure including: a first hierarchical datastructure comprising a first hierarchical score based on a plurality offirst-level elements, each of the plurality of first-level elementshaving a respective weighting, and a second hierarchical data structurecomprising a plurality of second hierarchical scores based on aplurality of second-level elements, each of the plurality ofsecond-level elements having a respective weighting, wherein the firsthierarchical score is based on the plurality of second hierarchicalscores through an index factor; a computing platform including computinghardware of at least one processor and memory operably coupled to the atleast one processor; and instructions that, when executed on thecomputing platform, cause the computing platform to implement: aregional monitor engine configured to manage local access to a pluralityof external data sources to coordinate writes to the index structure.

In an embodiment, a method for coordinating a plurality of data sourcescomprises providing an index structure including: a first hierarchicaldata structure comprising a first hierarchical score based on aplurality of first-level elements, each of the plurality of first-levelelements having a respective weighting, and a second hierarchical datastructure comprising a plurality of second hierarchical scores based ona plurality of second-level elements, each of the plurality ofsecond-level elements having a respective weighting, wherein the firsthierarchical score is based on the plurality of second hierarchicalscores through an index factor; managing local access to a plurality ofexternal data sources to coordinate writes to the index structure;updating the plurality of second-level elements from the plurality ofexternal data sources; verifying the update to the plurality ofsecond-level elements; evaluating the verified plurality of second-levelelements; applying the index factor; and calculating the plurality ofsecond hierarchical scores and the first hierarchical score.

In an embodiment, a system for coordinating a plurality of data sourcescomprises means for providing an index structure including: a firsthierarchical data structure comprising a first hierarchical score basedon a plurality of first-level elements, each of the plurality offirst-level elements having a respective weighting, and a secondhierarchical data structure comprising a plurality of secondhierarchical scores based on a plurality of second-level elements, eachof the plurality of second-level elements having a respective weighting,wherein the first hierarchical score is based on the plurality of secondhierarchical scores through an index factor; means for managing localaccess to a plurality of external data sources to coordinate writes tothe index structure; means for updating the index structure from theplurality of external data sources according to a time-based trigger oran event-based trigger; means for verifying the updated index structure;and means for analyzing the verified index structure by evaluating theverified plurality of second-level elements, applying the index factor,and calculating the plurality of second hierarchical scores and thefirst hierarchical score.

In an embodiment, the improved Smart Region architecture can comprisedynamically determining the boundaries of a Smart Region based on thesocial-cultural-economic orientation to a Smart City and a distributedcity model. A distributed city model is a representation of a number ofcommunity types in a distributive hierarchy. In an embodiment, adistributed city model can comprise a representation of six communitytypes, such as dispersed housing, small towns, regional centers,suburbs, inner city neighborhoods, and central business districts.

In an embodiment, the improved Smart Region architecture can comprisetelecenter networks to assist in connecting and educating members of acommunity. In embodiments, telecenter networks can be specific toregions, communities, or neighborhoods.

In an embodiment, the improved Smart Region architecture comprises aregional monitor that collects, stores, and organizes community datafrom multiple sources. The multiple data sources can be external to anindex structure. Accordingly, a regional monitor coordinates updates tothe data structure.

In an embodiment, the regional monitor comprises a regional profilingmodule. In an embodiment, the regional profiling module is configured toimplement geo-political regional profiles for each participating SmartRegion to ensure accurate comparisons between the Smart Regions. In anembodiment, geographic, social, and economic characteristics can berecorded under each regional profile. Characteristics of regionalprofiles can include population, ethnicity, demographics, eco-system,origin, employment base, fiscal stability, government, and per capitastatistics. In an embodiment, the regional profiling module can allowfor performance comparisons among participating regions regardless oflocation.

In an embodiment, the improved Smart Region architecture comprises aknowledge engine configured to maintain the integrity of the database,automatically perform all of the calculations that update the scoringprocess, extend the results of each element's changes throughout thesystem, and recalculate scores for components and regional profiles.

In an embodiment, the knowledge engine is configured to periodicallyanalyze input data for key indicators. In an embodiment the knowledgeengine categorizes key indicators into key levels.

In an embodiment, the knowledge engine comprises an input generatormodule to maintain the currency of the data. In an embodiment the inputgenerator module can update data on a time-based sequence or based onevent occurrence.

In an embodiment, the knowledge engine comprises an auditor module toverify the updated data. The auditor module can be responsible forupdating scoring interpretations, the contact person's contactinformation, changes to stakeholder profiles, and changes to regionalprofiles.

In an embodiment, the knowledge engine comprises an analysis module toproperly sequence execution of interactive processing to ensureconsistent, accurate scoring outcomes. In an embodiment, the analysismodule updates the evaluation input, and other sources, applies theweights, verifies the numeric equivalency values, and calculates thescore for each element. Once updated, the analysis module then updatesany data relationship changes, stakeholder alert changes, or othernecessary changes based on the input. In an embodiment, the analysismodule generates trends, histories, tracking, and forecasting reports.

In an embodiment, the knowledge engine comprises a report generatormodule to produce specialized reports based upon areas of interest,impact potential, and/or trends. In an embodiment, the report generatormodule automates distribution of the specialized reports based upon asubscription list.

In an embodiment, the knowledge engine comprises an inquiry module toprovide access to data through a web portal. In an embodiment, theinquiry module includes preformatted customizable reports with variablefield selection, sorting, and totaling functions. Additionally, theinquiry module can allow for new format requests of existing reports.

In an embodiment, the regional monitor comprises an index structureseparating components into levels of detail. In an embodiment, the indexstructure can weigh each component relative to the other componentswithin the assigned level of detail. In an embodiment, the indexstructure can further separate components into additional categories. Inan embodiment, the index structure can update components at differentfrequencies based on the rate of new data, ownership of the data, andthe best means of data transmittal.

In an embodiment, an evaluation grade can be calculated for eachcomponent. In an embodiment, the evaluation grades can be based upon anA, B, C classification system. In an embodiment the evaluation grade canbe converted into a numerically equivalent evaluation score using anexponential structure. In an embodiment, the evaluation score can bebased on at least one of the weights of each component in the indexstructure or the numerical equivalent of the evaluation grade. In anembodiment, the evaluation score can be an exponential value tohighlight the problematic components.

In an embodiment, a variety of elements relevant to the component can beconsidered when calculating an evaluation score. In such an embodiment,each element can have a specific weight and criteria set depending onthe relevance of the element to the evaluation score. Further, eachelement can have an extensive profile record of sub-elements. In anembodiment, element data is maintained by the input generator module,the auditor module, and the analysis module.

A primary differentiator over existing systems is that the regionalmonitor gathers inputs from the source closest to the actual source ofthe activity before sub-elements are developed. Accordingly, embodimentsprovide much better analysis if engaged as part of the Smart City/Regionarchitecture.

In an embodiment, data relationships between Smart Regions can be usedin addition to regional profiles to better identify interrelationshipsof comparative baselines. For example, data relationships can helpidentify common trends between Smart Regions including the impact ofsubsidies and waivers on competitors or rising fuel prices.

In an embodiment, the regional monitor is configured to identifystakeholders in the Smart Region. In an embodiment, identifiedstakeholders can include citizens, activists, politicians, tax payers,the community, corporations, or special interest groups.

In an embodiment, the regional monitor is further configured to measurecompliance. For example, with standards defined for each activity, ascoring mechanism is utilized to determine if each standard is beingachieved. In an embodiment, the scoring mechanism is based upon specificinterpretive benchmarks. In an embodiment, the scoring mechanism cancorrelate the compliance of each component with stakeholders that areengaged in operating the component.

In an embodiment, the regional monitor is further configured tocomprehensively analyze data. For example, with a comprehensive set ofdata, correlations can be calculated to shed new perspectives on a givenissue. Drawn from outside of the typical “vertical” orientation, suchexternal data can prove critically important in ensuring that servicesare being delivered cost-effectively and efficiently, as well as thatthe services are achieving the desired outcome.

In an embodiment, a community portal provides a radical departure fromthe concepts of an open Internet. A hyper-localized community portal isdriven by an artificial intelligence (AI) engine. For example, thecommunity portal can include a centralized website configured to createa critical mass identity for the community. In existing solutions, toooften if a user wants to learn something about a community, theinformation is buried behind many other websites for hotels, restaurantsetc. In contrast, the information of the community in the portal isindexed to provide integrated community information access. Individualwebsites within the community are not affected but rather integrated.Accordingly, the portal is the dominant site that provides a morefocused access to their information.

In an embodiment, a community portal includes an automated communityreceptionist. Because of the consolidation of websites, the ability toprovide a receptionist available any time becomes feasible. And becauseof the AI system backing, any user can get the answers they need quicklyand efficiently.

In an embodiment, a tech trek can be utilized to teach the systems andmethods described herein. A tech trek also allows the community todefine its own needs and access a funding set-aside.

In an embodiment, the tech trek itself is a three day conferencepreceded by a speaker's dinner. For example, the mornings are dedicatedto addressing detailed issues for each group's special interests. Theafternoons are dedicated to a directed discussion of specific topicsusing assigned seating to ensure integration of the group. The resultsof these discussions are analyzed overnight and new topic sheetsprepared for the next day. The focus will be how the groups can worktogether to implement programs to build better communities.

Registration for a tech trek conference is unique. Participants will beasked to register in a manner similar to that required for a collegedegree. Various “majors” are offered complete with required courses, anumbers of “credits,” and recommendations for “pick 2 of 3” offeringsfrom another discipline. Advanced “masters” and “doctorates” can beoffered with additional coursework as well.

In an embodiment, the improved Smart Region architecture can comprise ane-consensus forum that strategically engages community members to becomethe voice of the community through their understanding of ongoingdevelopment of the public and private sector. The e-consensus forum isdesigned to be anonymous, inclusive, democratic, focused, anddocumented.

In an embodiment, the e-consensus forum is configured to include anissues formulation forum module that examines multiple concerns andprioritizes issues that must be resolved. In an embodiment, the issueformulation forum module uses component weights and scoring to determineissue prioritization.

In an embodiment, the e-consensus forum is configured to include apolicy development forum module that brings stakeholders together todevelop a policy consensus around identified community issues.

In an embodiment, the e-consensus forum is configured to include a smartregion operations module that facilitates development of policies andoperating guidelines to establish Smart Region infrastructure.

In an embodiment, the e-consensus forum is configured to include anelection forum module that collects and categorizes community interestsaccumulated during an election period to hold elected stakeholdersaccountable.

In an embodiment, the e-consensus forum is configured to include anaccountability forum to organize a response to an inappropriate event oraction that has not been properly addressed.

In an embodiment, an e-consensus forum can be implemented on a processorthat ranks the top issues facing the topic of concern, votes on theresults and then builds on the top issue to explore necessarystrategies, implementation tactics and the assignment of implementationresponsibilities. Further, in certain embodiments, a consensus andaccountability forum can be implemented on the processor to balancepublic and private sectors.

In an embodiment, a device-based app supports the systems and methodsdescribed herein.

The above summary is not intended to describe each illustratedembodiment or every implementation of the subject matter hereof. Thefigures and the detailed description that follow more particularlyexemplify various embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

Subject matter hereof may be more completely understood in considerationof the following detailed description of various embodiments inconnection with the accompanying figures, in which:

FIG. 1 is a block diagram of a system implemented by a single cloudserver, according to an embodiment.

FIG. 2 is a block diagram of a system implemented by multiple cloudservers, according to an embodiment.

FIG. 3 is a block diagram of components of a Smart Region, according toan embodiment.

FIG. 4 is a flowchart of a regional virtualization action plan,according to an embodiment.

FIG. 5 is a block diagram of system engines, according to an embodiment.

FIG. 6 is a block diagram of an index scoring method for a regionalmonitor engine, according to an embodiment.

FIG. 7 is an annotated image of a value evaluation method for a regionalmonitor engine, according to an embodiment.

FIG. 8 is an example of a screenshot of a community portal enginedisplay, according to an embodiment.

FIG. 9 is diagram of a telecenter network, according to an embodiment.

FIG. 10 is a block diagram of a Smart Regionsystem, according to anembodiment.

FIG. 11 is a diagram of an index structure, according to an embodiment.

FIG. 12 is a diagram of weighting schema for elements, according to anembodiment.

FIG. 13 is a diagram of regional profile characteristics, according toan embodiment.

FIG. 14 is a map of potential Smart Regions across the United States,according to an embodiment.

FIG. 15 is a diagram of scoring interpretation for grading, according toan embodiment.

FIG. 16 is a diagram of numerical grade equivalence, according to anembodiment.

FIG. 17 is a diagram of scoring a component, according to an embodiment.

FIG. 18 is a chart of a relationship index for two region indexstructures, according to an embodiment.

FIG. 19 is a flowchart of a knowledge engine, according to anembodiment.

FIG. 20 is a flowchart of an input generator module, according to anembodiment.

FIG. 21 is a flowchart of an auditor module, according to an embodiment.

FIG. 22 is a diagram of an assessment review process, according to anembodiment.

FIG. 23 is a flowchart of an analysis module, according to anembodiment.

FIG. 24 is a diagram of a scoring update process, according to anembodiment.

FIG. 25 is a diagram and flow chart of a relationship update process,according to an embodiment.

FIG. 26 is a diagram and flowchart of a stakeholder relationship updateprocess, according to an embodiment.

FIG. 27 is a diagram and flowchart of a trends and tracking generationprocess, according to an embodiment.

FIG. 28 is a flowchart of a report generation module, according to anembodiment.

FIG. 29 is a diagram and flowchart of a report generation anddistribution process, according to an embodiment.

FIG. 30 is a flowchart of an inquiry module flow chart, according to anembodiment.

FIG. 31 is a flowchart of an e-consensus forum, according to anembodiment.

While various embodiments are amenable to various modifications andalternative forms, specifics thereof have been shown by way of examplein the drawings and will be described in detail. It should beunderstood, however, that the intention is not to limit the claimedinventions to the particular embodiments described. On the contrary, theintention is to cover all modifications, equivalents, and alternativesfalling within the spirit and scope of the subject matter as defined bythe claims.

DETAILED DESCRIPTION OF THE DRAWINGS

Referring to FIG. 1, a system 100 is described herein with respect to acloud-based system, embodiments of the invention can be performed in acloud computing, client-server, or standalone computer processingenvironment, or any combination thereof.

In an embodiment, cloud-based processing engine 102 generally includesserver 106 and database 108. Cloud-based processing engine 102 embodiesthe computation, software, data access, and storage services that areprovided to users over a network. The components of cloud-basedprocessing engine 102 can be located in a singular “cloud” or network,or spread among many clouds or networks, as depicted in FIG. 2. End-userknowledge of the physical location and configuration of components ofcloud-based processing engine 102 is not required.

Server 106 generally includes processor 110 and memory 112. Processor110 can be any programmable device that accepts digital data as input,is configured to process the input according to instructions oralgorithms, and provides results as outputs. In an embodiment, processor110 can be a central processing unit (CPU) configured to carry out theinstructions of a computer program. Processor 110 is thereforeconfigured to perform basic arithmetical, logical, and input/outputoperations.

Memory 112 can comprise volatile or non-volatile memory as required bythe coupled processor 110 to not only provide space to execute theinstructions or algorithms, but to provide the space to store theinstructions themselves. In embodiments, volatile memory can includerandom access memory (RAM), dynamic random access memory (DRAM), orstatic random access memory (SRAM), for example. In embodiments,non-volatile memory can include read-only memory, flash memory,ferroelectric RAM, hard disk, floppy disk, magnetic tape, or opticaldisc storage, for example. The foregoing lists in no way limit the typeof memory that can be used, as these embodiments are given only by wayof example and are not intended to limit the scope of the invention.

As depicted in FIG. 1, server 106 interfaces with database 108 viaprocessor 110. Specifically, processor 110 can execute database-specificcalls to store and retrieve data from database 108. Database 108 is cancomprise any organized collection of data. In embodiments, database 108can comprise simple non-volatile memory as part of a computer. Inembodiments, database 108 can comprise database management systems suchas Oracle, IBM DB2, or Microsoft SQL Server, for example. Inembodiments, database 108 actually comprises a plurality of databases.

In an embodiment, as shown in FIG. 1, database 108 is discrete fromserver 106. In another embodiment, database 108 is a part of server 106.In other embodiments, referring to FIG. 2, database 108 can be accessedas part of a separate cloud-based processing engine 102. Components ofcloud-based processing engine 102 can therefore be spread among multiplecloud-based processing engines. For example, and referring to system150, server 102 can be spread among many cloud-based processing engines102. Database 108 can reside on a first cloud-based processing engine102, while processor 110 and memory 112 reside on a second cloud-basedprocessing engine 102. Or, processor 110 can reside on a firstcloud-based processing engine 102, memory 112 can reside on a secondcloud-based processing engine 102, and database 108 can reside on athird cloud-based processing engine 102. Any number of permutationswhere components are spread among a plurality of clouds are considered.

Likewise, system 150 can also comprise a plurality of servers 106 anddatabases 108. User interface 104 can be configured to access a firstcloud-based processing engine 102, and the processing, storage, andpresentation of data can be configured to be spread among second andthird cloud-based processing engines 102 which are coupled to the othercloud-based processing engines 102, for example. Systems 100 and 150 aredepicted in FIGS. 1 and 2, respectively, for simplicity only by way ofexample and are not intended to limit the scope of the invention.

Referring to FIG. 3, a block diagram of components of a Smart Region 200is depicted, according to an embodiment. More particularly, FIG. 3depicts example infrastructure that can be applied to implement a SmartRegion.

In an embodiment, Smart Region 200 generally comprises a teleworkprogram audit 202. In an embodiment, telework program audit 202 isconfigured to indicate a level of compliance of a teleworkimplementation.

Smart Region 200 further comprises feasibility analysis 204. In anembodiment, feasibility analysis 204 is configured to output thebenefits of a strategic approach for an organization's fiscal andoperational position.

Smart Region 200 further comprises workforce virtualization program 206.In an embodiment, workforce virtualization program 206 is configured tooptimize telework deployments, function as part of a continuity/disasterprogram, implement performance accountability, and facility“rightsizing.” In an embodiment, telework program audit 202, feasibilityanalysis 204, and workforce virtualization program 206 can beemployer-dependent.

Smart Region 200 further comprises deployment partnership 208. In anembodiment, deployment partnership 208 represents the validation of thetelecommuting mindset in the region and embraces collaboration with theprivate sector in addressing all forms of telecommuting for congestionand quality of life concerns. Further, deployment partnership 208 isconfigured to utilize statistics to better understand industry policyand best practices, employer deployment profiles, infrastructureanalysis and their impacts on all other metro systems. In an embodiment,deployment partnership 208 can be region-dependent.

Smart Region 200 further comprises distributed city model 210, regionalvirtualization action plan 212, and virtual transportation managementorganization 214.

In an embodiment, distributed city model 210 is configured with a suiteof implementation tools to enhance the viability and quality of life forall communities and their residents. For example, community profilesprovide access to rural communities for technology and transit andprovide tools for individual rural communities to populate informationon the Internet.

In an embodiment, regional virtualization action plan 212 is configuredwith various s to engage the community to review quality of lifeopportunities that telecommuting offers and to modify public and privatepolicy in its support, and provide a multi-phased program to engage thecommunity in a discussion about virtualization and desires to applyvirtualization to eliminate the urban-rural divide.

For example, referring to FIG. 4, a flowchart of a regionalvirtualization action plan 212 is depicted, according to an embodiment.Regional virtualization action plan 212 can include a four-phase programconfigured to gather information on the number of applications that cansupport the telecommuting mindset, i.e., to make everything that isavailable physically, available electronically, i.e., to access“anything” from “anywhere,” at “anytime”. In other words it seeksphysical-electronic “access parity” for all services deemed necessaryfor the lifestyle.

In addition, four congestion techniques are deployed: (1) routine tripreduction as a result of employee deployment, (2) four-level deploymentthat occurs when activated by participating employer's continuity plans,(3) short term traffic diversions and increased deployments for trafficdisruptions, and (4) long-term deployments during road reconstructionand other significant traffic interruptions.

In one embodiment, regional virtualization action plan 212 is performedin four consecutive Phases. First, an Occupational Assessment (Phase I)begins with a Regional Workshop to review the Assessment process withall participating agencies. The group reviews the Assessment Report anddetermines if there is any additional data that could be collected tobetter understand the telework potential of the region. In anembodiment, forty-five employers (three employers in each of the fifteenbusiness sectors) are recruited to participate. The assessment isadministered to the assigned employees that represent the designatedoccupations.

Once all employers have completed the Assessment the data is analyzedand extended to the regional workforce. The report is prepared, whichcan address trips, accidents, energy, emissions, space reductionpotential, managerial understanding of telework, attitudes about avariety of topics and each employer gets their individual report alongwith a graph indicating their success probability and return oninvestment.

In Phase II, the Community Audit expands the Phase I findings byconducting a curriculum audit for participating colleges/universities.This Assessment determines the degree to which distance learning couldbe applied to lecture courses among others to determine its impact ontrips and potential space reductions and building repurposing

In Phase III, community visioning is based upon an e-consensus forum.All of the preceding data is provided to groups of community membersprior to the Forum. At the forum, the group is asked to comment on theadvisability of adopting the telecommuting mindset, determining whatmust be done to encourage and/or expand telecommuting applications. Alist of these changes is drafted for implementation and the appropriateagency is identified to make the change.

In Phase IV, an implementation monitor tracks the list of implementationrecommendations and supports the development and adoption of therecommended policies and standards.

In an embodiment, virtual transportation management organization 214 isconfigured with various s to support the region by addressingcongestion, emergency management, and by extending virtual services tonon-transit areas. Virtual transportation management organization 214supports and promotes telecommuting applications and applies suchapplications as a comprehensive alternative to physical travel.

As depicted, distributed city model 210, regional virtualization actionplan 212, and virtual transportation management organization 214 areinputs to a further distributed city model 216.

Smart Region 200 further comprises community design suite 218. In anembodiment, community design suite 218 provides a set of tools forindividual communities to maximize the opportunities and benefits of itsengagement in Smart Region 200.

Smart Region 200 further comprises smart region initiative 220. In anembodiment, smart region initiative 220 comprises full integration ofthe urban-rural divide throughout the metropolitan service area.

In an embodiment, regional monitor 222 is described herein further withrespect to FIGS. 5-7, and can be implemented in coordination with policyand best practices research database 224.

Referring to FIG. 5, a block diagram of a system 300 is depicted,according to an embodiment. In an embodiment, system 300 comprises aregional monitor engine 302, a community portal engine 304, a tech trekengine 306, and a forum engine 308.

Regional monitor engine 302 comprises a management informationsub-system for a region as defined by the Distributed City Model/SmartRegion. In an embodiment, the sub-system comprises three elements.

First, the sub-system comprises a profile for the region that isdeveloped based upon a set of universal factors. This allows comparisonsbetween comparable regions world-wide.

Second, the sub-system comprises a high-level computational process thatanalyzes all data fed into the sub-system to maintain monthly trend datafor key indicators at three or more levels: a base level for each of theareas of raw data collection (e.g., how many riders in each of transitcategory); a mid-level that analyzes all transit operations to generatea transportation index, and a high level index that evaluates allsub-indices into a comprehensive view of all trends.

Third, the sub-system comprises a data collection component that focuseson data at the lowest possible level. Each index may have numeroussources of index data. For example, a cell phone app can send messagesto each data source requesting the specific data for which it isresponsible. Referring to FIG. 6, a block diagram of an index scoringmethod for regional monitor engine 302 is depicted, according to anembodiment.

Embodiments are configured to develop a 1-10 range for all scoresthrough interpolation and define an appropriate score for each index.For example, the range of sales per capita may range from $50-$2500.Accordingly, through interpolation, 1=50 and 10=2500. The appropriatescoring process can decide that a score of 3.5 would be the optimalscore (a dollar value of $857). With a standardized 1-10 format, thedeviations from the optimal are easier to track.

For costs, the currency conversion rates as a separate item can betracked. In an embodiment, all costs would be converted to dollars.

The computational process that builds the index consists of three valuescombined to constitute the score. In an embodiment, a definitional valueis given an alpha grade based upon definitions, thereby creatingconsistency. In an embodiment, the definitional values ae converted tonumeric values with an exaggeration of the negative. Accordingly, areally bad value massively alters the overall score (e.g. in lettergrades, A=2, a B=4, a C=6 and a D=16). Finally, a community value‘weighting’ differentiate items that are very important to a particularcommunity from those that are not. For example, referring to FIG. 7, anannotated image of a value evaluation method for regional monitor engine302 is depicted, according to an embodiment. As will be described, afurther embodiment of evaluation methods are disclosed by FIG. 22 andits corresponding description.

In an embodiment, factors to be considered include physical systemsfactors such as geology (e.g. unique features, mineral resources, slopestability/rockfall, depth to impermeable layer, subsistence,consolidation, weathering/chemical release, and tectonicactivity/volcanism); soils (e.g. slope stability, erodingcharacteristics, first value, and foundation support—shrink swell, frostsusceptibility, surface deformation, liquefaction); special features(e.g. sanitary landfills, wetlands, central zone/shorelines, minedumps/spoil areas, and prime agriculture area); water (e.g. hydrologicbalance, ground water resources including existence of water and flowdirection depth to water table, sedimentation, water quality, anddrainage/storage including channel formation, impoundment leaks/slopefailure, flooding); climate/air quality (e.g. macro-climate hazards,forest/range fires, heat balance, wind alteration, humidity andprecipitation generation and dispersion of contaminants, shadow effects,and noise).

In an embodiment, additional factors to be considered include supportsystems factors such as biota (e.g. plant and animal species, vegetativecommunity, diversity, productivity, and nutrient cycling); energy (e.g.energy requirements, conservation measures, and environmentalsignificance); utilities (e.g. liquid water disposal, solid wastedisposal, water supply, and storm water drainage); and safety andtransportation (e.g. structures, materials, site hazards, circulationconflicts, road safety and design, and ionizing radiation).

In an embodiment, additional factors to be considered include communityservices factors (e.g. education, day care, libraries, social services,police, fire protection, commercial facilities, recreational, culturaland social services, employment, and health care hospitals—long termcare, physicians, and emergency health care).

In an embodiment, additional factors to be considered include communitystructure factors (e.g. psych-well being, physical threat, crowding,nuisance, and symbolism); aesthetic quality (e.g. visual content, areaand structure coherence, and apparent access); sense of community (e.g.community and organizations, homogeneity and diversity, and communitystability and physical characteristics); historic values (e.g. historicstructures, and archeological sites and structures); and physiowell-being (e.g noise, vibrations, odor, light, temperature, anddisease).

In an embodiment, additional factors to be considered include communityfiscal factors (e.g. budgetary accountability, donation tracking,institutional funding, oversight and analysis tools, tax rates, levies,equitability in financing).

Referring again to FIG. 5, community portal engine 304 generallycomprises a web server with a user interface display for a communitywith particular capabilities. For example, user interface 104 can beutilized to access content on the web server. The community portalengine 304 solves the problem of localized Internet search. Intraditional methods, if a user searches for a particular city, the webdelivers hotel, restaurant and other sites, but finding the “community”is a challenge.

The web server is configured to create a central website for thecommunity with specific capabilities. For example, referring to FIG. 8,a screenshot of an example community portal engine display is depicted,according to an embodiment. In an embodiment, a 24×7×365 receptionist isavailable to users to automatically respond to user questions. In anembodiment, an index structure provides data from multiple sources tocentralize such data in a single location. In an embodiment, an AIsystem helps users find exactly what they want. In an embodiment, aninterface for trade center neighborhoods or lifestyle neighborhoods canbe presented.

Referring again to FIG. 5, tech trek engine 306 is configured toorganize a plurality of events and attendees in a data structure. Forexample, user interface 104 can be utilized to access content or providecontent to tech trek engine 306.

In an embodiment, data collected and subsequently presented in anorganized format to a user can include community development data, suchas economic development, chambers of commerce, planners, locally electedofficials, etc.; strategic policy data such as national leaders inpolicy development roles, roles of government, corporations, educationetc. in developing and implementing cohesive policy responses forbuilding better communities; telecommunications data such ascommunications and computer companies; trip technologies data such asthe entre-preneurial fringe who are exploring trip replacement productsand services; and work relationships data.

Referring again to FIG. 5, forum engine 306 is configured to store andrank various data. For example, an e-consensus forum tool can beimplemented such that participants are random representatives of aconsensus and accountability forum (as will be described).Pre-registration requires a completed and verifiable profile so thatwhen the forum's final report is released, forum engine 306 candetermine what biases might be inherent in the group.

Using AI and machine learning, forum engine 306 can utilize data setsthat keep attributes anonymous, because the input is done throughcomputer interfaces, participants can express their true beliefs ratherthan having to publicly maintain a “party line.” In an embodiment,attributes are inclusive given various timeframes open for respondentsto participate. In an embodiment, attributes are democratic such thateach participant has one vote. Accordingly, rather than having anin-person meeting dominated by the most vocal or powerful individuals,ideas are all anonymous and therefore the participants vote on the valueof the ideas, not the strength of the personalities. In an embodiment,attributes are focused such that the process groups all the input into“vote-able” items so that the group or forum engine 306 can identifywhat is most important and leave the lesser issues for a later forum ifdesired. In an embodiment, once a vote is taken, the process movesforward. In an embodiment, attributes are recorded such that every wordand every vote is anonymously captured for later review and analysis.

In an embodiment, forum engine 306 provides sequential exploration of atopic in which the s alternate between an open-ended discussion aboutstrategies, tactics, and responsibilities followed by a vote after each.In an embodiment, only the top issue is moved to the next level.

In an embodiment, a consensus and accountability forum uses thee-consensus forum tool to re-establish the community's role as thefulcrum in keeping the public and private sectors in balance. Ane-consensus session is initiated automatically or by a user whenever asignificant issue develops. Participants are self-selected, but multiplesessions may be conducted when appropriate. The pre-registration profileis important to illustrate the contributors to these recommendations.The issues are analyzed and the recommendations documented and forwardedto the media and the politicians. In an embodiment, all of the issuesare reviewed and a political profile is developed showing whichlegislators voted with the recommendations. For major offices, anotherforum can be conducted to discuss the voting record compared to variouscandidates' records.

In an embodiment, a mobile device-based app can implement the variousfunctionality described herein. For example, system 300 can be madeavailable on the app by user interface 104, referring to FIGS. 1, 2, and5.

In an embodiment, during app setup, participants are presentedinterfaces to complete a profile of their interests, etc. Inembodiments, each forum comprises its own profile. Participants thenregister for a particular forum (link to personal and forum profiles) tocreate a roster and then a number of canned reports to determineinternal biases (e.g. age, place of residence, industry, politicalparty, etc.)

In operation, interfaces present an opening question posted to allparticipants (either at a single location or off-site where the user islocated). Participant users can enter their response without characteror input limits. Embodiments are configured to project all of responsesthrough networked interfaces, a video projector or print the results. Auser can then review all responses. Embodiments are further configuredto generate a list of the ideas presented in the first responses andpresent the ideas into a voting format. Next, the voting formatpresentation is presented to all participants with a variety of votingoptions. In one embodiment, an individual rating system for each optionon a 10-point scale is used. Upon receiving voting results, statisticalanalysis is conducted. In an embodiment, result images are generated andsubsequently returned to the users for review. In embodiments, theaforementioned cycle is repeated with a new question being posted toeveryone based upon the voting results.

In an embodiment, an improved Smart Region architecture is configured todynamically determine the boundaries of a Smart Region based on thesocial-cultural-economic orientation to a Smart City and a distributedcity model. A distributed city model is a representation of a number ofcommunity types in a distributive hierarchy. In an embodiment, adistributed city model can include a representation of six communitytypes, such as dispersed housing, small towns, regional centers,suburbs, inner city neighborhoods, and central business districts asshown in FIG. 9. Incorporating community types in a distributed citymodel based on social-cultural-economic orientation better classifiesattributes of each community within the Smart Region and allows forincreased accuracy when defining regional boundaries.

Referring again to FIG. 9, in an embodiment, the improved Smart Regionarchitecture comprises telecenter networks to assist in connecting andeducating members of a community. Telecenter networks support thetechnological and social interconnections underpinning economicintegration within a community by providing comprehensive access toinformation on both the public and private sectors. In some embodiments,telecenter networks can be specific to regions, communities, orneighborhoods.

Referring to FIG. 10, a block diagram of an improved Smart Regionarchitecture is depicted, according to an embodiment. In an embodiment,the improved Smart Region architecture comprises an index structure 402,a regional monitor 404, and a knowledge engine 406.

Index structure 402 is configured to comprehensively index components byvarious levels of detail. In an embodiment, index structure 402 isconfigured to store weights of each component relative to othercomponents within the assigned level of detail. In such an embodiment,the weights of each component effectively balance relevance andsignificance in order to generate an accurate perspective on the overallquality of region health. In an embodiment, sub-components can comprisecomponents. In an embodiment, weights can be scaled such that allsub-components within a component total a value of 100 when aggregated.Referring to FIG. 11, a diagram of an index structure 500 comprisingcomponents 502 and weights 504 is depicted.

In an embodiment, index structure 402 further includes or identifies aplurality of stakeholder groups. In such an embodiment, stakeholdergroups can include citizens, activists, politicians, taxpayers, specialinterest groups, community organizations, and corporations. In anembodiment, index structure 402 establishes correlations betweencomponents and relevant stakeholders who are engaged in maintaining thecomponents. Therefore, it is possible to determine if critical assetsare being supported properly by each shareholder or if a particularshareholder is failing to maintain certain component data.

In an embodiment, index structure 402 is further configured to index avariety of elements for each component. In an embodiment, each elementcan have a set of specific objective criteria that is used to determinethe relevance of the element to the overall component. In such anembodiment, each element can have a specific weight corresponding to therelevance of the element. Furthermore, each element can have anextensive profile record of sub-elements, as shown in FIG. 12. In anembodiment, weights can be scaled such that all sub-elements within anelement total a value of 100 when aggregated. For each element there isan extensive profile record of unit costs, goals, and other metrics toallow for status monitoring. Determinations of how each element of acomponent is functioning is a baseline for the index scoring structure.Regional monitor 404 comprises a regional profiling module and a datacollection coordination module. In an embodiment, the regional profilingmodule is configured to implement geo-political regional profilesdeveloped for each participating Smart Region to ensure accuratecomparisons between Smart Regions. The regional profiling moduleadditionally serves to identify key differentiating elements todistinguish the effects of profile differences from operating and policydifferences. In an embodiment, geographic, social, and economiccharacteristics can be recorded under each regional profile, as shown inFIG. 13. In such an embodiment, characteristics of regional profiles caninclude population, ethnicity, demographics, eco-system, origin,employment base, fiscal stability, government, and per capitastatistics. In an embodiment, regional monitor 404 can capture andprocess raw data from each Smart Region to allow for interactive andcomprehensive analysis of at least one component based on the regionalprofiling. In an embodiment, regional profiling can allow forperformance comparisons among participating regions regardless oflocation. Referring to FIG. 14, a map of some exemplary Smart Regionsthat may have performances compared is depicted.

Referring to FIG. 18, a chart of a relationship index for two regionindex structures is depicted. Comparing two Smart Regions with similarcomparative baselines can help identify interrelationships.Interrelationships that may affect multiple Smart Regions include anincrease in the cost of fuel, subsidies for certain products, andnatural disasters.

Referring again to FIG. 10, knowledge engine 406 is configured tomaintain the integrity of the data and data structures, automaticallyperform all of the calculations that update the scoring process, extendthe results of each element's changes throughout the system, andrecalculate scores for components and regional profiles within indexstructure 402. In an embodiment, knowledge engine 406 categorizescomponents into levels. For example, knowledge engine 406 can categorizehow many riders use each type of transit as a base level index,categorize all transit operations as transportation at a mid-levelindex, and categorize all sub-indices into a comprehensive view of allcategories at a high level index.

Referring to FIG. 19, a flowchart of a generalized overview of knowledgeengine components is depicted.

In an embodiment, knowledge engine 406 comprises an input generatormodule to maintain the currency of the data. In an embodiment, the inputgenerator module can update data at different frequencies on atime-based sequence or based on event occurrence. In an embodiment,change logs for historical tracking are maintained for each element andcomponent. In an embodiment, a contact person or preliminary contactmodule is responsible for the updating of the grade assigned to eachelement. In an embodiment, a contact supervisor or supervisory contactmodule is responsible for changes to stakeholders identification andother components.

Referring to FIG. 20, a flowchart of an input generator module process600 is depicted. At stored data 602, the database is opened. At extract604, the element tables are scanned to determine which need to beupdated based on their linked criteria sets. At process 606, aformatted, editable grading interpretation form for each element iscompiled. The grading interpretation form includes the current score,the grading interpretation descriptions and a field for updating. In anembodiment, any comments filed would also be incorporated into thegrading interpretation form. At cell phone interface 608, the gradinginterpretation form is sent via a cell phone interface to each contactperson for review. At manual input 610, the contact person completestheir review and updates the grade or indicates no change. At cell phoneinterface 612, the updated grading interpretation form is then returnedvia the cell phone interface. At predefined process 614, a scanningprocess monitors the returning data records and continues to re-transmitthem until the sent grading interpretation forms are returned. At sort616, a secondary process aggregates the returned grading interpretationforms and sends the returned grading interpretation forms to thecontract supervisor via the cell phone interface. In an embodiment, thecontact supervisor is responsible for signing off on the accuracy of thegrading interpretation forms, enhancing the ongoing integrity of thesystem. At decision 618, the input generator module process for elementsis complete and notifications for any necessary follow-up are sent. Atpredefined process 620, system managers direct the database operators tointroduce the updates into the system. At internal storage 622, theposting process updates all elements and sets any flags for furtherprocessing. At terminator 624, the database is updated and secured orbacked up.

In an embodiment, knowledge engine 406 comprises an auditor module toverify the updated data. The auditor module can be responsible forupdating scoring interpretations, contact information, changes tostakeholder profiles, and changes to regional profiles. In anembodiment, the auditor module is configured to periodically analyzeinput data for new elements and components within index structure 402.

Referring to FIG. 21, a flowchart of an auditor module process 700 isdepicted. At stored data 702, the database is opened. At extract 704,the auditor module accesses the input records. At process 706, the inputmodule sorts the input records by contact supervisor or supervisorycontact module and prepares an input review form containing old andupdated values. At cell phone interface 708 the auditor module transmitsthe input review form to all appropriate supervisors via the cell phoneinterface. At manual input 710, contact supervisors update elementvalues and overall records. At cell phone interface 712, the inputreview form is returned to the auditor module. At predefined process714, the input data is incorporated into the updating system by theauditor module. At decision 716 the updates are sent to an analysismodule. At terminator 718, the auditor module process 700 is ended.

In an embodiment, knowledge engine 406 comprises an analysis moduleconfigured to sequence execution of interactive processing to ensureconsistent, accurate scoring outcomes. In an embodiment, the analysismodule updates the evaluation input, and other sources where applicable,applies the weights, verifies the numeric equivalency values, andcalculates the score for each element, as depicted in FIG. 22. Onceupdated, the analysis module then updates any data relationship changes,stakeholder alert changes, or other changes based on the input. In anembodiment, the analysis module generates trends, histories, tracking,and forecasting reports.

Referring to FIG. 23, an analysis module process 800 is depicted. Atstored data 802, the database is opened. At extract 804, the analysismodule searches for all changes in the elements record. At predefinedprocess 806, the analysis module updates all element scores and extendsthose changes through the components. At predefined process 808, theanalysis module reviews all score changes and recalibrates thestakeholder and data relationship impact tables. At predefined process810, the analysis module computes and populates the trends, histories,tracking, and forecast tables. At predefined process 812, the analysismodule generates reports for stakeholders and other recipients. Atpredefined 814, the analysis module generates any error reports. Atterminator 816, the analysis module process 800 is terminated.

In an embodiment, the analysis module is configured to calculate anevaluation grade for each component, as depicted in FIG. 15. In anembodiment, the evaluation grades can be based upon an A, B, Cclassification system. In an embodiment, the evaluation grade can beconverted into a numerically equivalent evaluation score using anexponential structure, as shown in FIG. 16. The evaluation score can bean exponential value to highlight problematic components. In anembodiment, the evaluation score can be based on at least one of theweights of each component in the index structure or the numericalequivalent of the evaluation grade. Referring to FIG. 17, a diagram of asample scoring table for a component is depicted. In an embodiment, theelements relevant to each component are considered when calculating anevaluation score. In an embodiment, a predefined set of assessmentinterpretations are defined by the grading system.

Referring to FIG. 24, a diagram of the analysis module scoring multiplelevels of elements is depicted. The evaluation of each element appliesthe grades, numeric equivalence, and weighting from sub-elements tocalculate the element score.

In an embodiment, a relationship update process handles the scoring andrelationship changes that result each time an element is changed. Achange in an element's score can trigger cascading effects ascomponents, stakeholders, and relationships that depend on the elementwould have to be updated as well.

Referring to FIG. 25, a diagram and flowchart of a relationship updateprocess 900 is depicted. At stored data 902, the analysis module opensthe database. At extract 904, the analysis module identifies allelements that have been updated. At predefined process 906, for eachelement the analysis module creates a multi-dimensional arraycorrelating the components, stakeholders and relationships. Atpredefined process 908, the analysis module updates the scoringcalculations from the lowest level of components to the highest level ofcomponents and the index score. At predefined process 910, the analysismodule terminates relationship update process 900.

In an embodiment, upon completion of the updating process, the trendsand tracking generation process begins. Key reports are generated withinthe system for each updating cycle. Overtime, it is important to be ableto examine those changes within the context of policy and time. Thetrends and tracking generation process writes selective report formatsinto archival records to preserve the timeline

Referring to FIG. 26, a diagram and flowchart of a stakeholderrelationship update process 1000 is depicted, according to anembodiment. In an embodiment, relationship update process 900 andstakeholder relationship update process 1000 can be implementedsimultaneously, concurrently or sequentially, as the two processes caninclude identical actions (except for the fields being modified). Inparticular, at stored data 1002, the analysis module opens the database.At extract 1004, the analysis module identifies all elements that havebeen updated. At predefined process 1006, for each element the analysismodule creates a multi-dimensional array correlating the components,stakeholders and relationships. At predefined process 1008, the analysismodule updates the scoring calculations from the lowest level ofcomponents to the highest level of components and the index score. Atpredefined process 1010, the analysis module terminates relationshipupdate process 1000.

Referring to FIG. 27, a diagram and flowchart of a trends and trackinggeneration process 1100 is depicted. At stored data 1102, the analysismodule opens the database. At extract 1104, the analysis modulesequentially selects a current report set. At predefined process 1106,the analysis module writes a new record in each trend and tracking tablereflective of the current report's content. At terminator 1108, thetrends and tracking generation process is ended. In an embodiment,knowledge engine 406 comprises a report generator module to producespecialized reports based upon areas of interest, impact potential, andtrend analysis. In an embodiment, the report generator module automatesdistribution of the specialized reports based upon a subscription list.Accessing important information during policy development can becritical. The report generator module is designed to maximize theopportunity to dynamically correct data.

Referring to FIG. 28, a diagram and flowchart of a report generatormodule process 1200 is depicted. At display 1202, the report generatormodule opens a report generator screen. At select your area of interest1204, the primary area of interest is selected based on the desirednature of the report. At select your correlated area of interest 1206, alist of potential areas of interest that correlate to the primary areaof interest are displayed. At select your report format 1208, thedesired format of the report from the format list is selected. At selectthe fields for your report 1210, the fields to be displayed areselected. At choose your search and sort options 1212, the searchcriteria and sort order for the eligible fields are set. At specify thetotaling and sub totaling options 1214, the totaling and sub-totalingoptions for the eligible fields are set. At select output options 1216,the output parameters are set. At terminator 1218, the report isgenerated according to the determined specifications.

In an embodiment, upon completion of the updating process, the reportgeneration and tracking process begins. In an embodiment, pre-designreport formats are assessed and the appropriate multi-dimensional arraysare generated within the system for each report generation and trackingprocess.

Referring to FIG. 29, a diagram and flowchart of a report generation andtracking process 1300 is depicted. At stored data 1302, the reportgeneration module opens the database. At extract 1304, the reportgeneration module sequentially opens all report format records. Atpredefined process 1306, a multi-dimensional array is created for eachreport, and the necessary calculations are executed for each report. Atterminator 1310, the report generation module ends the process.

In an embodiment, knowledge engine 406 comprises an inquiry module toprovide access to data through a web portal. In an embodiment theinquiry module preformatted customizable reports with variable fieldselection, sorting, and totaling functions. Additionally, the inquirymodule can allow for new format requests of existing reports.

Referring to FIG. 30, a flowchart of an inquiry module process 1400 isdepicted. At display 1402, the inquiry module opens the report generatorscreen. At select area of interest 1404, the primary area of interest isselected based on the desired nature of the inquiry. At establishmulti-dimensional array 1406, a multi-dimensional array is establishedfor components mating the area of interest. At select your elements1408, a list of elements potentially correlated to the area of interestare displayed and the relevant elements are selected. At select thefields for your report 1410, the fields to be displayed are selected. Atchoose your search and sort options 1412, the search criteria and sortorder for the eligible fields are set. At specify the totaling and subtotaling options 1414, the totaling and sub-totaling options for theeligible fields are set. At select output options 1416, the outputparameters are set. At terminator 1418, the report is generatedaccording to the determined specifications.

In an embodiment, the improved Smart Region architecture can comprise ane-consensus forum that strategically engages community members to becomethe voice of the community through their understanding of ongoingdevelopment of the public and private sector. The e-consensus forum isdesigned to be anonymous, inclusive, democratic, focused, anddocumented.

In an embodiment, the e-consensus forum is configured to include anissues formulation forum module that examines multiple concerns andprioritizes issues that must be resolved. In an embodiment, the issueformulation forum module uses component weights and scoring to determineissue prioritization.

In an embodiment, the e-consensus forum is configured to include apolicy development forum module that brings stakeholders together todevelop a policy consensus around identified community issues.

In an embodiment, the e-consensus forum is configured to include a smartregion operations module that facilitates development of policies andoperating guidelines to establish Smart Region infrastructure.

In an embodiment, the e-consensus forum is configured to include anelection forum module that collects and categorizes community interestsaccumulated during an election period to hold elected stakeholdersaccountable.

In an embodiment, the e-consensus forum is configured to include anaccountability forum to organize a response to an inappropriate event oraction that has not been properly addressed.

Referring to FIG. 31, a flowchart of an e-consensus forum process 1500is depicted. At participant recruitment 1502, individuals respond to theoutreach effort and a statistical profile is developed for each forum todetermine if inherent biases are present. At issues modification 1504,forums are identified, designed, and scheduled for launch based onmultiple inputs. At registration 1506, individuals are selected orregister to participate in the chosen forum. At the forum 1508, theforums are conducted on or off site. At regional monitor resources 1510,resources for the forum participants to consider are provided as desiredboth before and during the forum. At predefined process 1512, theresults are formatted for distribution upon conclusion of the forum. Atmedia, follow up forum, and participant profile s 1514, a profile of theparticipants is prepared and incorporated into a report which isdistributed to the media and relevant stakeholders. At internal storage1516, the full documentation of the forum is archived in a reachableformat.

Various embodiments of systems, devices, and methods have been describedherein. These embodiments are given only by way of example and are notintended to limit the scope of the claimed inventions. It should beappreciated, moreover, that the various features of the embodiments thathave been described may be combined in various ways to produce numerousadditional embodiments. Moreover, while various materials, dimensions,shapes, configurations and locations, etc. have been described for usewith disclosed embodiments, others besides those disclosed may beutilized without exceeding the scope of the claimed inventions.

Persons of ordinary skill in the relevant arts will recognize that thesubject matter hereof may comprise fewer features than illustrated inany individual embodiment described above. The embodiments describedherein are not meant to be an exhaustive presentation of the ways inwhich the various features of the subject matter hereof may be combined.Accordingly, the embodiments are not mutually exclusive combinations offeatures; rather, the various embodiments can comprise a combination ofdifferent individual features selected from different individualembodiments, as understood by persons of ordinary skill in the art.Moreover, elements described with respect to one embodiment can beimplemented in other embodiments even when not described in suchembodiments unless otherwise noted.

Although a dependent claim may refer in the claims to a specificcombination with one or more other claims, other embodiments can alsoinclude a combination of the dependent claim with the subject matter ofeach other dependent claim or a combination of one or more features withother dependent or independent claims. Such combinations are proposedherein unless it is stated that a specific combination is not intended.

Any incorporation by reference of documents above is limited such thatno subject matter is incorporated that is contrary to the explicitdisclosure herein. Any incorporation by reference of documents above isfurther limited such that no claims included in the documents areincorporated by reference herein. Any incorporation by reference ofdocuments above is yet further limited such that any definitionsprovided in the documents are not incorporated by reference hereinunless expressly included herein.

For purposes of interpreting the claims, it is expressly intended thatthe provisions of 35 U.S.C. § 112(f) are not to be invoked unless thespecific terms “means for” or “for” are recited in a claim.

The invention claimed is:
 1. A system for coordinating a plurality ofdata sources, the system comprising: an index structure including: afirst hierarchical data structure comprising a first hierarchical scorebased on a plurality of first-level elements, each of the plurality offirst-level elements having a respective weighting, and a secondhierarchical data structure comprising a plurality of secondhierarchical scores based on a plurality of second-level elements, eachof the plurality of second-level elements having a respective weighting,wherein the first hierarchical score is based on the plurality of secondhierarchical scores through an index factor; a computing platformincluding computing hardware of at least one processor and memoryoperably coupled to the at least one processor; and instructions that,when executed on the computing platform, cause the computing platform toimplement: a regional monitor engine configured to manage local accessto a plurality of external data sources to coordinate writes to theindex structure.
 2. The system of claim 1, wherein the index factor mapsa weighting to the first hierarchical score by at least one of theplurality of second hierarchical scores.
 3. The system of claim 1,wherein the index structure further includes a third hierarchical datastructure comprising a plurality of third hierarchical scores based on aplurality of third-level elements, and wherein at least one of theplurality of second hierarchical scores and the first hierarchical scoreare based on the plurality of third hierarchical scores through theindex factor.
 4. The system of claim 1, wherein the instructions that,when executed on the computing platform, cause the computing platform tofurther implement: a knowledge engine including: an input generatorengine configured to automatically update the plurality of second-levelelements, an auditor engine configured to automatically verify theupdate to the plurality of second-level elements, and an analyst engineconfigured to automatically evaluate the verified plurality ofsecond-level elements, apply the index factor, and calculate theplurality of second hierarchical scores and the first hierarchicalscore.
 5. The system of claim 4, wherein the input generator engine isconfigured to automatically update the plurality of second-levelelements according to a time-based trigger or an event-based trigger. 6.The system of claim 4, wherein the analyst engine is further configuredto: apply a first regional profile to a first instance of the indexstructure for a first region; apply a second regional profile to asecond instance of the index structure for a second region; and generatea comparison between the first region and the second region.
 7. Thesystem of claim 6, where in the first regional profile comprises aplurality of first profile elements, and the second regional profilecomprises a plurality of second profile elements, wherein the firstregional profile and the second regional profile have shared elementsbut different values for the plurality of first profile elements and theplurality of second profile elements.
 8. The system of claim 4, whereinthe knowledge engine further includes: a report generator engineconfigured to create a pre-defined report based on the firsthierarchical score and the plurality of second hierarchical scores; andan inquirer engine configured to provide a user interface to access thepre-defined report.
 9. The system of claim 8, wherein the inquirerengine is further configured to: receive a request for a dynamic reportvia the user interface, the dynamic report including a plurality ofcriteria, and wherein the report generator engine is further configuredto create the dynamic report based on the plurality of criteria.
 10. Amethod for coordinating a plurality of data sources, comprising:providing an index structure including: a first hierarchical datastructure comprising a first hierarchical score based on a plurality offirst-level elements, each of the plurality of first-level elementshaving a respective weighting, and a second hierarchical data structurecomprising a plurality of second hierarchical scores based on aplurality of second-level elements, each of the plurality ofsecond-level elements having a respective weighting, wherein the firsthierarchical score is based on the plurality of second hierarchicalscores through an index factor; managing local access to a plurality ofexternal data sources to coordinate writes to the index structure;updating the plurality of second-level elements from the plurality ofexternal data sources; verifying the update to the plurality ofsecond-level elements; evaluating the verified plurality of second-levelelements; applying the index factor; and calculating the plurality ofsecond hierarchical scores and the first hierarchical score.
 11. Themethod of claim 10, further comprising: identifying, profiling, andcontrolling a plurality of regional indicators to define the pluralityof second-level elements.
 12. The method of claim 10, wherein applyingthe index factor maps a weighting to the first hierarchical score by atleast one of the plurality of second hierarchical scores.
 13. The methodof claim 10, further comprising: automatically updating the plurality ofsecond-level elements according to a time-based trigger or anevent-based trigger.
 14. The method of claim 10, further comprising:applying a first regional profile to a first instance of the indexstructure for a first region; applying a second regional profile to asecond instance of the index structure for a second region; andgenerating a comparison between the first region and the second region.15. The method of claim 14, where in the first regional profilecomprises a plurality of first profile elements, and the second regionalprofile comprises a plurality of second profile elements, wherein thefirst regional profile and the second regional profile have sharedelements but different values for the plurality of first profileelements and the plurality of second profile elements.
 16. The method ofclaim 10, further comprising: creating a pre-defined report based on thefirst hierarchical score and the plurality of second hierarchicalscores; and providing a user interface to access the pre-defined report.17. The method of claim 16, further comprising: receiving a request fora dynamic report via the user interface, the dynamic report including aplurality of criteria, and creating the dynamic report based on theplurality of criteria.
 18. A system for coordinating a plurality of datasources, the system comprising: means for providing an index structureincluding: a first hierarchical data structure comprising a firsthierarchical score based on a plurality of first-level elements, each ofthe plurality of first-level elements having a respective weighting, anda second hierarchical data structure comprising a plurality of secondhierarchical scores based on a plurality of second-level elements, eachof the plurality of second-level elements having a respective weighting,wherein the first hierarchical score is based on the plurality of secondhierarchical scores through an index factor; means for managing localaccess to a plurality of external data sources to coordinate writes tothe index structure; means for updating the index structure from theplurality of external data sources according to a time-based trigger oran event-based trigger; means for verifying the updated index structure;and means for analyzing the verified index structure by evaluating theverified plurality of second-level elements, applying the index factor,and calculating the plurality of second hierarchical scores and thefirst hierarchical score.